def gmain(ch="216501166744497073410"): from learn import Learn endtime = 128 data = Learn().build_with_User_table_for_prog(slicer=endtime) print(len(data[0])) data = [(line[0], line[1], Wisard.retinify([ float(t) - float(t0) + 10 for t, t0 in zip(line[3:endtime], line[2:endtime]) ])) for line in data] print( "Tabela gerada por rede neural sem peso para derivada segunda do tempo com prognóstico da carla" ) # bleacher = dict(V=805, S=-6, E=81, F=154) # bleacher = dict(V=1485, S=-359, E=34, F=139) 199321270259550360019 v, s, f, e, b, a, d =\ int(ch[:4]), int(ch[4:7]), int(ch[7:10]), int(ch[10:13]), 0, 600, 10 bleacher = dict(V=v, S=s, E=e, F=f) w = Wisard(data, 32 * endtime, bleach=b, mapper=bleacher, enf=a, sup=d) # bleacher = dict(V=893, S=-304, E=-48, F=25) # w = Wisard(data, 32 * endtime, bleach=995, mapper=bleacher, enf=452, sup=39, unsupervised=unsupervised) # bleacher = dict(V=603, S=0, E=81, F=154) # w = Wisard(data, 32 * endtime, bleach=600, mapper=bleacher, enf=110, sup=20) w.main() print(len(data[0][2:]))
def main(_=0, unsupervised=False): from learn import Learn endtime = 128 data = Learn().build_with_User_table_for_prog(slicer=endtime) print(len(data[0])) data = [(line[0], line[1], Wisard.retinify([ float(t) - float(t0) + 10 for t, t0 in zip(line[3:endtime], line[2:endtime]) ])) for line in data] print( "Tabela gerada por rede neural sem peso para derivada segunda do tempo com prognóstico da carla" ) # bleacher = dict(V=805, S=-6, E=81, F=154) # 83.36 16.64 v:2165, s:11, f:667, e:422, b:970, a:734, d:10 bleacher = dict(V=1202, S=-15, E=59, F=165) # 199321270259550360019 # bleacher = dict(V=1615, S=-15, E=42, F=169) # 199321270259550360019 # bleacher = dict(V=2531, S=169, E=634, F=856) # w = Wisard(data, 32 * endtime, bleach=913, mapper=bleacher, enf=609, sup=18, unsupervised=unsupervised) w = Wisard(data, 32 * endtime, bleach=913, mapper=bleacher, enf=600, sup=10, unsupervised=unsupervised) # bleacher = dict(V=893, S=-304, E=-48, F=25) # w = Wisard(data, 32 * endtime, bleach=995, mapper=bleacher, enf=452, sup=39, unsupervised=unsupervised) # bleacher = dict(V=603, S=0, E=81, F=154) # w = Wisard(data, 32 * endtime, bleach=600, mapper=bleacher, enf=110, sup=20) w.main() print(len(data[0][2:])) print(w.single()) """
def init(self): self.colorlib = self.madcow.colorlib try: self.learn = Learn(madcow=madcow) except: self.learn = None self.google = Google()
def __init__(self, master=None): Application_ui.__init__(self, master) self.logger = MyLogger(log_board=self.log_board, filename='all.log', level='debug') self.learn = Learn(self.logger) self.t1 = None
def init(self): try: self.default_location = settings.YELP_DEFAULT_LOCATION except: self.default_location = DEFAULT_LOCATION try: self.learn = Learn(madcow=self.madcow) except: self.learn = None
def run(self): try: Learn(files=self.files, update_file=self.file, top=self.top) except: traceback.print_exc() exctype, value = sys.exc_info()[:2] self.signals.error.emit((exctype, value, traceback.format_exc())) else: self.signals.result.emit(None) finally: self.signals.finished.emit(True)
def init(self): opts = {} for key, default in self.defaults.iteritems(): setting = 'WUNDERGROUND_' + key.upper() val = getattr(settings, setting, None) if val is None: val = default opts[key] = val self.api = WeatherUnderground(log=self.madcow.log, **opts) self.learn = Learn(madcow=self.madcow) self.method_triggers = [(getattr(self.api, method_name), triggers, pws) for method_name, triggers, pws in self._method_triggers]
def __init__(self, tier, room, bot): self.learn = Learn(room) self.opponent = {} self.generation = "generation 6" self.bot = bot self.weather = "" self.statuses = [] self.opponent_pokemon_team = [] self.do_not_switch = False self.ws = self.bot.ws self.team = None self.strongest_move = "" self.active = "" self.id = "" self.tier = tier self.room = room self.turn = 0
def open_app(app_name, cells, audio, arduino): current_app = None app_name = app_name.replace(" ", "") if app_name == 'riddles': current_app = Riddles("Riddles", cells, audio, arduino) elif app_name == 'learn': current_app = Learn("Learn", cells, audio, arduino) elif app_name == 'tutor': current_app = Tutor("Tutor", cells, audio, arduino) elif app_name == 'headlines': current_app = Headlines("Headlines", cells, audio, arduino) elif app_name == 'memory': current_app = Memory("Memory", cells, audio, arduino) if current_app is not None: audio.speak("Opening the application " + app_name) current_app.on_start() else: audio.speak( "I did not recognize the app. Could you try to open the app again?" )
def open_app(self, app_name): current_app = None app_name = app_name.replace(" ", "") if app_name == 'riddles': current_app = Riddles("Riddles") elif app_name == 'learn': current_app = Learn("Learn") elif app_name == 'tutor': current_app = Tutor("Tutor") elif app_name == 'headlines': current_app = Headlines("Headlines") elif app_name == 'memory': current_app = Memory("Memory") if current_app is not None: glob.mainApp.audio.speak("Opening the application") glob.mainApp.audio.speak(current_app.name) current_app.on_start() else: # shouldn't occur glob.mainApp.audio.speak( "I did not recognize the app. Could you try to open the app again?" )
def __init__(self, madcow): self.learn = Learn(madcow)
def __init__(self, madcow=None): self.weather = Weather() try: self.learn = Learn(madcow=madcow) except: self.learn = None
def init(self): colorlib = self.madcow.colorlib self.weather = Weather(colorlib, self.log) self.learn = Learn(madcow=self.madcow)
def init(self): self.learn = Learn(madcow=self.madcow) self.staff = Staff(madcow=self.madcow)
# Scheduler scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=20, verbose=False, threshold=0.0001, threshold_mode='rel', cooldown=0, min_lr=1e-07, eps=1e-08) # Learning class learn = Learn(args, train_loader=train_loader, validate_loader=valid_loader, test_loader=test_loader, train_set=train_set, validate_set=valid_set, test_set=test_set) # %% # ----------------------------------------------------------- # # Losses functions # # ----------------------------------------------------------- print('[Creating criterion]') # Losses if args.model in ['ae', 'vae', 'wae', 'vae-flow']: criterion = nn.MSELoss() if args.num_classes > 1:
def __init__(self, madcow): self.learn = Learn(madcow) self.config = madcow.config
def init(self): self.colorlib = self.madcow.colorlib try: self.learn = Learn(madcow=self.madcow) except: self.learn = None
def init(self): self.learn = Learn(madcow=self.madcow) self.staff = Staff(madcow=self.madcow) self.company = Company(madcow=self.madcow) self.realname = Realname(madcow=self.madcow) self.notes = Notes(madcow=self.madcow)
def init(self): self.learn = Learn(self.madcow)
from learn import Learn, Classification import argparse import curses #the only extra argument is "learn" which is used to train the svm parser = argparse.ArgumentParser(description='Recognize who is typing') parser.add_argument('--learn', help='teach the algorithm to recognize the user', action='store_true') args = parser.parse_args() stdscr = curses.initscr() #need to know the name of the user for predictive purposes if args.learn: Learn(stdscr) else: Classification(stdscr)
from learn import Learn from decimal import * from re import findall learn_path = "./new_train" learn = Learn(learn_path) # print(learn.number_of_bad_words, learn.number_of_good_words) module = learn.number_of_good_words + learn.number_of_bad_words probably_of_good_examp=Decimal(learn.number_of_good_example / learn.number_of_examles) probably_of_bad_examp=Decimal(learn.number_of_bad_example / learn.number_of_examles) CONST_OF_IMPORTANT=3 def probability_b(word): bad, good = 0, 0 try: good = learn.good[word] bad = learn.bad[word] except: pass numb = bad+good if numb <= CONST_OF_IMPORTANT: return Decimal(0.5) return Decimal(bad/numb) def probability_g(word): bad, good = 0, 0 try: good = learn.good[word] bad = learn.bad[word]
def main(): Game = GameClass() Svs = Learn(100000) Game.Play(Svs)